In PET, each line of response (LOR) has different sensitivity due to many factors, including crystal efficiency, geometry profile, and count-rate dependent block profile. If not corrected properly before image reconstruction, the variation in LOR sensitivity will lead to quantitative error and artifacts.
Component-based PET detector normalization approaches, in which the geometry profile, crystal efficiency, and detector block profile are estimated and corrected separately, have been widely attempted for 3D PET detector efficiency normalization.
For example, in component-based PET detector normalization, data is acquired at different count rates, and then used to generate different count-rate-dependent normalization coefficients (NCs) using true events only or prompt events (with random and scatter corrected events). Alternatively, data is acquired at a low count rate, and then extrapolated to a high count rate. A combination of the above two approaches has also been used. However, such techniques have significant drawbacks.